Fintech cos like CapitalFloat, LoanTap are using bots to decide if you're eligible for a loan

Kirana store owners and cab drivers applying for a loan from fintech startup CapitalFloat have to answer a series of questions around their financial planning as well as their business practices, formatted by the Bengaluru-based company as a psychometric test to gauge credit-worthiness.

Alternative lending startups, which do not demand collateral or use CIBIL scores like traditional banks, are using machine learning and innovative methods such as psychometric tests to ensure loans don't sour.

"The tests revolve around understanding the person's entrepreneurial ability and attitude towards financial planning. Two-thirds of our customers are new to credit and do not have any tangible credit scores, which is why such tests help in understanding their credit-worthiness," said cofounder Sashank Rishyasringa, adding that the company also looks at several data points such as mobile phone top-up recharges or ecommerce sales while assessing an application.

CapitalFloat has disbursed loans worth Rs 700 crore to SMEs and self-employed individuals in the three years of its existence, with a rejection rate of 50%, and less than 1% of those have turned out to be bad loans.

Mumbai-based fintech startup LoanTap, which was formed this year and started disbursing personal loans only in August, has seen 100% EMI returns on the 116 loans that it has given out so far with an average ticket size of Rs 2.4 lakh.

"It is a very good sign for us that our filters and assessment methods are working. We are using machine learning to predict exactly what size of loan is suitable for a particular customer," said Satyam Kumar, executive director at LoanTap. "The data points we use include getting to know the customer's job stability through LinkedIn or their lifestyle patterns through Facebook. Lendingkart, which lends to SMEs, has built a proprietary technology in the form of a credit knowledge database that instantly throws up industry trends such as operating cycles to understand the relative performance of the SME."

"We have also built on APIs of government sites to understand the tax filing behaviour of customers. We use Natural Language Processing (NLP) to get data and observations on the performance of loans and the customer's business and use machine learning to help identify patterns," said CEO Harshvardhan Lunia.

"Essentially, the entire screening process is automated, which enables us to facilitate a loan in a matter of hours," Lunia said, adding that the rejection rate of loan applications is as high as 75%.

Fintech startups are also using algorithms to help banks and NBFCs to lend securely. "We allow lenders to assess fraud risks and credit risks by generating up to 10,000 data points around financial behaviour patterns of customer, including points such as whether the applicant spends most of his money early in the month," said Abhishek Agarwal, founder of CreditVidya that works with 8 banks and NBFC partners.